Abstract
Time series forecasting is a challenge in several areas and for several applications. A series of tools have emerged to tackle this problem, from classic models to modern models that use machine learning and deep learning. One of these areas of great interest is financial. Specifically, forecasting the prices of stocks and indices can be especially difficult due to the characteristics of this type of series. In this work, we propose a method to automatically develop a deep network using a genetic algorithm to select the hyperparameters. To test the proposed method, we used four datasets, including financial and non-financial time series. In both financial and non-financial datasets, the proposed method with automated hyperparameter selection did better than the models made by other authors using different methods.
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Urbinate, E., Itano, F., Del-Moral-Hernandez, E. (2023). CNN-LSTM Optimized by Genetic Algorithm in Time Series Forecasting: An Automatic Method to Use Deep Learning. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_25
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